Infrared technology has been applied in fields of biology, medical science, geosciences, military reconnaissance, etc. Compared with visible light image, the infrared image having a lower signal-to-noise ratio is easily contaminated by different noises, wherein the stripe noise is one of the noises often existing in the infrared image. The reasons of causing the stripe noise are complicated, for example, process differences of multi-sensor, aging of the instrument and elements, and the internal calibration system error, etc. which result in different conversion transfer function for various detection unit. The external environmental interferences (e.g. temperature, the other ambient apparatuses) during the acquisition process of the infrared image is also one of the reasons. The detection and extraction for the useful information in an infrared image are interfered by the presence of stripe noises, particularly the recognition and target tracking capability based on the infrared image are affected thereby. Accordingly, the use of stripe noise removal algorithms first eliminating the stripe noise in the infrared image will greatly improve the reliability of the subsequent processing and analysis for the infrared image.
The stripe noise in the images is typically a periodic noise, which, represented in the frequency domain, is the appearance of noise components in fixed frequency points in the frequency domain. In order to filter out such periodic noise frequency components, it is first necessary to find out the corresponding positions of the noise frequency components in the frequency domain, then the notch filter is used to filter out the noise, and finally the signal is restored to the time-domain or spatial-domain. The chief of all is how to find the correct noise frequency point, and how to design a proper notch filter.
One of the problems in the methods for eliminating stripes in images in prior art is that the position determination of the noise frequency component is not intelligent or accurate. One of the conventional methods uses manual detection for the corresponding positions of the noise frequency components, which is a time-consuming method with low calculation efficiency that cannot meet the application requirement of a large data, and is affected by manual subjective factors; another method in the prior art is to locate the frequency point of stripe noise according to the image projection of the amplitude spectrum. For this method, the amplitude spectrum of the image is respectively projected in a row direction and a column direction, the location of the noise frequency component is subsequently obtained by a cumulative distribution function after projection. Compared with the manual method, the efficiency is improved by the present method.
The method for eliminating stripe noise in images in the prior art has another problem i.e. the problem about periodic alignment. Only when the collected signals have stringent full cycle, are the noise frequency components of the periodic noise in the image concentrated to limited frequency points. If the stripe noise in the image does not have a stringent full cycle, the noise frequency components will be differently diffused in the entire frequency domain. If prior to the filtering, the image is not aligned with the pre-treatment period, the use of a notch filter method cannot completely filter out the stripe noise frequency components.